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1. Evaluating the power and limitations of genome-wide association studies in Caenorhabditis elegans

   https://doi.org/10.1093/g3journal/jkac114  

   Widmayer, Samuel J; Evans, Kathryn S; Zdraljevic, Stefan; Andersen, Erik C (May 2022, G3 Genes|Genomes|Genetics)

   Macdonald, S (Ed.)

   Abstract Quantitative genetics in Caenorhabditis elegans seeks to identify naturally segregating genetic variants that underlie complex traits. Genome-wide association studies scan the genome for individual genetic variants that are significantly correlated with phenotypic variation in a population, or quantitative trait loci. Genome-wide association studies are a popular choice for quantitative genetic analyses because the quantitative trait loci that are discovered segregate in natural populations. Despite numerous successful mapping experiments, the empirical performance of genome-wide association study has not, to date, been formally evaluated in C. elegans. We developed an open-source genome-wide association study pipeline called NemaScan and used a simulation-based approach to provide benchmarks of mapping performance in collections of wild C. elegans strains. Simulated trait heritability and complexity determined the spectrum of quantitative trait loci detected by genome-wide association studies. Power to detect smaller-effect quantitative trait loci increased with the number of strains sampled from the C. elegans Natural Diversity Resource. Population structure was a major driver of variation in mapping performance, with populations shaped by recent selection exhibiting significantly lower false discovery rates than populations composed of more divergent strains. We also recapitulated previous genome-wide association studies of experimentally validated quantitative trait variants. Our simulation-based evaluation of performance provides the community with critical context to pursue quantitative genetic studies using the C. elegans Natural Diversity Resource to elucidate the genetic basis of complex traits in C. elegans natural populations. 

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2. Successful Invasion Into New Environments Without Evidence of Rapid Adaptation by a Predatory Marine Gastropod

   https://doi.org/10.1111/mec.17575  

   Bentley, Blair P; Cheng, Brian S; Brennan, Reid S; Swenson, John D; Adkins, Jamie L; Villeneuve, Andrew R; Komoroske, Lisa M (December 2024, Molecular Ecology)

   ABSTRACT Invasive species with native ranges spanning strong environmental gradients are well suited for examining the roles of selection and population history in rapid adaptation to new habitats, providing insight into potential evolutionary responses to climate change. The Atlantic oyster drill (Urosalpinx cinerea) is a marine snail whose native range spans the strongest coastal latitudinal temperature gradient in the world, with invasive populations established on the US Pacific coast. Here, we leverage this system using genome‐wide SNPs and environmental data to examine invasion history and identify genotype–environment associations indicative of local adaptation across the native range, and then assess evidence for allelic frequency shifts that would signal rapid adaptation within invasive populations. We demonstrate strong genetic structuring among native regions which aligns with life history expectations, identifying southern New England as the source of invasive populations. Then, we identify putatively thermally adaptive loci across the native range but find no evidence of allele frequency shifts in invasive populations that suggest rapid adaptation to new environments. Our results indicate that while these loci may underpin local thermal adaptation in their native range, selection is relaxed in invasive populations, perhaps due to complex polygenic architecture underlying thermal traits and/or standing capacity for phenotypic plasticity. Given the prolific invasion ofUrosalpinx, our study suggests population success in new environments is influenced by factors other than selection on standing genetic variation that underlies local adaptation in the native range and highlights the importance of considering population history and environmental selection pressures when evaluating adaptive capacity. 

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3. A few essential genetic loci distinguish Penstemon species with flowers adapted to pollination by bees or hummingbirds

   https://doi.org/10.1371/journal.pbio.3002294  

   Wessinger, Carolyn A.; Katzer, Amanda M.; Hime, Paul M.; Rausher, Mark D.; Kelly, John K.; Hileman, Lena C. (September 2023, PLOS Biology)

   Barton, Nick H. (Ed.)

   In the formation of species, adaptation by natural selection generates distinct combinations of traits that function well together. The maintenance of adaptive trait combinations in the face of gene flow depends on the strength and nature of selection acting on the underlying genetic loci. Floral pollination syndromes exemplify the evolution of trait combinations adaptive for particular pollinators. The North American wildflower genusPenstemondisplays remarkable floral syndrome convergence, with at least 20 separate lineages that have evolved from ancestral bee pollination syndrome (wide blue-purple flowers that present a landing platform for bees and small amounts of nectar) to hummingbird pollination syndrome (bright red narrowly tubular flowers offering copious nectar). Related taxa that differ in floral syndrome offer an attractive opportunity to examine the genomic basis of complex trait divergence. In this study, we characterized genomic divergence among 229 individuals from aPenstemonspecies complex that includes both bee and hummingbird floral syndromes. Field plants are easily classified into species based on phenotypic differences and hybrids displaying intermediate floral syndromes are rare. Despite unambiguous phenotypic differences, genome-wide differentiation between species is minimal. Hummingbird-adapted populations are more genetically similar to nearby bee-adapted populations than to geographically distant hummingbird-adapted populations, in terms of genome-wided_XY. However, a small number of genetic loci are strongly differentiated between species. These approximately 20 “species-diagnostic loci,” which appear to have nearly fixed differences between pollination syndromes, are sprinkled throughout the genome in high recombination regions. Several map closely to previously established floral trait quantitative trait loci (QTLs). The striking difference between the diagnostic loci and the genome as whole suggests strong selection to maintain distinct combinations of traits, but with sufficient gene flow to homogenize the genomic background. A surprisingly small number of alleles confer phenotypic differences that form the basis of species identity in this species complex. 

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4. A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types

   https://doi.org/10.3389/fgene.2020.587887  

   Zhu, Huanhuan; Shang, Lulu; Zhou, Xiang (January 2021, Frontiers in Genetics)

   Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types. 

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5. Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

   https://doi.org/10.7554/eLife.73983  

   Nguyen Ba, Alex N; Lawrence, Katherine R; Rego-Costa, Artur; Gopalakrishnan, Shreyas; Temko, Daniel; Michor, Franziska; Desai, Michael M (February 2022, eLife)

   Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits. 

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